Abhijeet Ravankar , Ankit A. Ravankar , Michiko Watanabe and Yohei Hoshino Paper Link image Courtesy: the Verge Public places like hospitals and industries are required to maintain standards of hygiene and cleanliness. Traditionally, the cleaning task has been performed by people. However, due to various factors like shortage of workers, unavailability of 24-h service, or health concerns related to working with toxic chemicals used for cleaning, autonomous robots have been seen as alternatives. In recent years, cleaning robots like Roomba have gained popularity. These cleaning robots have limited battery power, and therefore, efficient cleaning is important. Efforts are being undertaken to improve the efficiency of cleaning robots. The most rudimentary type of cleaning robot is the one with bump sensors and encoders, which simply keeps cleaning the room while the battery has charge. Other approaches use dirt sensors attached to the robot to clean only the untidy portions of the floor
Sho Yokoi, Ryo Takahashi ,Reina Akama, Jun Suzuki, Kentaro Inui Abstract A key principle in assessing textual similarity is measuring the degree of semantic overlap between two texts by considering the word alignment. Such alignment-based approaches are intuitive and interpretable; however, they are empirically inferior to the simple cosine similarity between general-purpose sentence vectors. To address this issue, we focus on and demonstrate the fact that the norm of word vectors is a good proxy for word importance, and their angle is a good proxy for word similarity. Alignment-based approaches do not distinguish them, whereas sentence-vector approaches automatically use the norm as the word importance. Accordingly, we propose a method that first decouples word vectors into their norm and direction, and then computes alignment-based similarity using earth mover’s distance (i.e., optimal transport cost), which we refer to as word rotator’s distance. Besides, we find how to “grow” the